Journal of Earth System Science最新文献

Understanding the rainfall variability is crucial for managing water resources and mitigating agricultural hazards, particularly in poorly gauged regions like the Abaya–Chamo basin. This study compares various satellite-derived rainfall products, including Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Climate Hazards Group Infrared Precipitation (CHIRP), with observed rainfall data from 1990 to 2019. Accordingly, this study evaluates the performance of these satellite rainfall products using multiple metrics at daily and monthly scales. The correlation coefficient (CC), mean square error (MSE), Nash-Sutcliffe efficiency (NSE), percent of bias (PBIAS), mean absolute error (MAE), and categorical analysis metrics such as probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI) indicators were applied to evaluate the accuracy of these products. Among them, the CHIRPS satellite product demonstrates superior agreement with observed data, with CC = 0.871 and NSE = 0.925, warranting its selection for further analysis of seasonal and annual rainfall variability. The coefficient of variation (CV) and precipitation concentration index (PCI) were applied to investigate rainfall variability. The study indicates that precipitation patterns in the Abaya–Chamo basin exhibit moderate to high variability throughout the year, with a CV ranging from 20–30%. This suggests substantial variability in annual rainfall within the region, in some instances where the variability exceeds 30%. Moreover, the southern and northern regions of the basin experience a consistent moderate to high variation in precipitation throughout the entire season, while the lowest variability was observed in the central part of the basin. These findings underscore the importance of satellite-derived rainfall data, particularly the CHIRPS product, in understanding spatiotemporal rainfall patterns and making informed decisions in water resource management. This research contributes in advancing our knowledge of rainfall variability in the Abaya–Chamo basin and underscores the utility of satellite data in regions lacking adequate ground-based monitoring.

Abstract

Crust and upper mantle discontinuities play a key role in understanding continental formation and evolution. The most prevalent seismic techniques, like receiver function, surface wave tomography, etc., face problems of multiples from shallow crustal discontinuities and low vertical resolution, respectively, which makes it difficult to image deeper discontinuities. To get the better of these complications and image the deeper discontinuities with greater accuracy, the P-wave autocorrelation method has been used for the teleseismic data recorded at Hyderabad station (HYB) in south India. This method has efficiently identified the major shallow upper mantle discontinuities down to 250 km depth. The Moho, mid-lithospheric discontinuity, Hales discontinuity, lithosphere–asthenosphere boundary and Lehmann discontinuity were observed at 30.37, 92.17, 123.43, 140.50 and ~201 km, respectively. We also achieved a very high vertical resolution (<0.6 km) for all the shallow upper mantle discontinuities. Further, we also proposed an iterative method to calculate the ({v}_{p}/{v}_{s}) ratio of the crust, using the arrival times of Moho reflected (2p) and (p+s) phase. Unlike other seismic methods, this iterative method is independent of any constraint on ({v}_{p}) and ({v}_{s}). The ({v}_{p}/{v}_{s}) is found to be 1.744, suggesting the crust beneath HYB is felsic in nature.

Research highlights

• Shallow upper mantle discontinuities imaged beneath Hyderabad (HYB) station with high vertical resolution (<0.6 km)

• A thin crust (30 km) and felsic composition (({v}_{p}/{v}_{s}sim 1.744)) beneath HYB.

• Distinct MLD and LAB signatures at ~92 km and ~140 km, respectively, beneath HYB.

• A diffused Hales discontinuity (123 km) and Lehmann Discontinuity (201 km) are also observed.

This study presents a comprehensive comparison between traditional hydrological models and advanced machine learning (ML) techniques in predicting streamflow dynamics. Traditional models, namely the Soil and Water Assessment Tool (SWAT) and Génie Rural à 4 Paramètres Journalier (GR4J), are juxtaposed against ML models, including Random Forest (RF), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Both SWAT and GR4J demonstrated commendable performance, with GR4J displaying marginally superior predictive accuracy, evidenced by its tighter RMSE values. In the realm of ML, RF exhibited exceptional prowess in integrating diverse climatic features, especially in a scenario integrating comprehensive meteorological data. ANN showcased consistent performance across different input scenarios, emphasising its robustness. LSTM and BiLSTM, tailored for time series data, underscored the importance of precipitation’s temporal dynamics in streamflow predictions. A notable revelation is the significance of choosing appropriate input data, with certain scenarios outperforming others based on the amalgamation of meteorological parameters. The flow duration curve (FDC) analysis further highlighted the model capabilities, with RF and BiLSTM excelling in capturing extreme flows, while traditional models resonated more with medium flow regimes. This research offers vital insights for hydrologists and decision-makers, aiding in informed model selection for streamflow predictions.

With the high demand for fossil fuels, exploring the frontier areas for hydrocarbon reserves has become imperative. The recent discoveries in Gojalia, Sonamura, Baramura, and Sundalbari fields emphasize the need to explore additional anticlinal structures in Tripura for hydrocarbon exploration. Tulamura anticline (the study area) produced gas from Upper Bhuban, establishing hydrocarbon prospectivity in the northern part, but the southern part remains largely unexplored. An electro-log interpretation revealed the presence of sand facies deposited in a fining upward sequence, suggesting channel deposition. An integrated geophysical approach using seismic inversion and machine learning techniques was performed to delineate and characterize the litho-facies dispersal patterns in the Tulamura field. Spectral decomposition (12, 20 and 28 Hz) of stacked seismic data were RGB (red-green-blue) blended, revealing the southward striking channel geometry of the Bhuban Formation at a depth of 2220 m. The 3D P-impedance and Vp/Vs ratio volumes were estimated using the model-based pre-stack seismic inversion. Inversion results help discriminate among sand, shale and siltstone litho-facies. Petrophysical property (effective porosity) was predicted by combining the post-stack seismic attributes and well-log data using neural network modelling. The identified sand facies within the channel geometry exhibit relatively moderate to low P-impedance (9800–10600 m/s * gm/cm³), low Vp/Vs ratio (1.68–1.76), and moderately high effective porosity (8–13%) from surroundings, indicating favourable conditions for hydrocarbon accumulations. Shale between channels and major faults can create favourable stratigraphic entrapment, while an upward fining sequence suggests an intact top seal. This study advocates an integrated approach involving geophysical inversion and machine learning to identify optimal conditions for hydrocarbon accumulation within sand facies, supported by structural and stratigraphic entrapment.

The Cauvery Basin is one of the prolific hydrocarbon-producing basins of southern India. The pericratonic basin has five sub-basins separated by five basement and structural highs formed by granitic and gneissic rocks of the Archaean Southern Granulite Terrane. The sub-basins have excellent Cretaceous source rocks and hydrocarbon-bearing reservoirs of different geological ages. Commercial hydrocarbons are produced from the Archean basement highs, specifically from the Kumbakonam–Madanam palaeo-highs of the Cauvery basin. The paper addresses basement characterisation using conventional as well as advanced well logs for accurate characterisation of the basement reservoirs. Basement reservoirs are challenging in terms of sporadic porosity and permeability distribution. Most of the porosities and permeability are attributed to secondary generation by fracturing and weathering. The present work aims to understand and compare the petrophysical attributes of two kinds of basement reservoirs (fractured basement and weathered basement) on the Kumbakonam–Madanam High. Two wells from each type lying on either side of the Madanam High were selected. Well log data, including gamma-ray, resistivity, porosity (neutron and bulk density), acoustic logs and advanced logs, such as resistivity images and dipole acoustic and elemental capture spectroscopy logs, from these four wells were analysed for their petrophysical understanding. Furthermore, image and acoustic log data were integrated to characterise the fracture geometry and fracture permeability of the basement reservoirs. Conventional log suites showing low GR, low resistivity, high density, and high neutron porosity are indicative of intricate lithologies, possibly mafic rocks. A notable negative crossover in density and neutron log along with excess Si concentration indicates weathering. A higher fracture density with a crisscross fracture/mesh fracture pattern is indicated by the analysis of image logs, shear wave anisotropy, and Stoneley fractures. Thus, a comparison of the petrophysical attributes of both fields is attempted to understand the fractured and weathered basement reservoirs and their geological characteristics. In conclusion, the basement reservoirs of the Madanam High Field are of interest because of their hydrocarbon-producing ability, and proper synthesis of petrophysical attributes will help develop activities in the basement reservoirs.

Surajkund Formation of Central Narmada Basin exhibits fining upward sequences of pebbly conglomerate, coarse-fine grained sandstone, siltstone and association of seven lithofacies, namely massive pebbly conglomerate, coarse-medium grained sandstone with large scale tabular cross bedding, massive coarse grained sandstone, coarse to medium grained sandstone with horizontal parallel bedding, fine grained sandstone with parallel lamination, fine grained sandstone with ripple lamination and siltstone, indicates their deposition in mixed load meandering river. Granulometric studies of Surajkund sediments also support the fluvial depositional environment. Soft sediment deformation structures documented in the siltstones suggest sediment liquification due to earthquake shocks. Abundant development of nodular, bedded calcretes and rhizoliths within these sediments are indicative of semi-arid climate and related subaerial exposure. These sediments are prominently lithic arenites, and clay mineralogy as well as geochemistry indicate deposition in the proximity of source, short distance of transport and mixed provenance of a variety of pre-Quaternary rocks such as Precambrian metamorphic rocks and granites, Vindhyan and Gondwana Supergroups, Deccan Trap basalt and laterite. Evidences of fresh water phreatic as well as vadose zone diagenesis linked to the semi-arid climatic conditions, together with subaerial exposure of these sediments, are seen in thin sections, which are supported by δ¹³C (av. −5.67%) and δ¹⁸O (av. −3.88%) values of calcretes. These values also suggest calcretes formed due to pedogenic and shallow groundwater processes in warm climate with C4-dominated vegetation. OSL date of one sample from Surajkund Formation gave an Ionian Age of Pleistocene Epoch.

Identification of landslide susceptible zones is the preliminary step to plan mitigation measures in landslide-prone mountainous terrains. The use of various machine learning (ML) algorithms has proven their superiority in terms of enhancing the success rate in susceptibility studies. Therefore, the present study focuses on spatial prediction of landslides using integrated supervised and unsupervised machine learning (ML) techniques with reference to Bhagirathi Valley, NW Himalaya. A landslide inventory of 514 landslides and 14 viable causative factors of landslides in the study area have been selected for the analysis. Three efficient supervised ML techniques, i.e., random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbour (KNN), have been integrated with an unsupervised ISODATA cluster classification technique to prepare the landslide susceptible maps (LSM) of the study area. All the models depict that the greater part of the high and very high landslide hazard zones lie in the Main Central Thrust zone and its vicinity in the Bhagirathi Valley. The accuracy of each model was determined and compared using several statistical signifiers like sensitivity, specificity, area under curve, accuracy, and Kappa index. The results show that XGBoost and RF models exhibit higher performance accuracy than KNN. The quantitative assessment of prepared LSMs of the study area was also done using frequency ratio (FR) and frequency density (FD). The results indicate the consistency of each model in the prediction of landslide zones in the study area as FR and FD both increase with the increase of landslide susceptibility levels from very low to very high in all the models.

Abstract

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the ‘very high’ landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction.

Research Highlights

• A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier.

• Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide.

• The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95.

• The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022.

Wetlands are characterized by flow and environmental dispersion, which are necessary in the water management system. Contaminant transport in wetlands has a significant effect in the field of biology and environmental fluid dynamics for the conservation of fish and wildlife species, erosion prevention, and mainly recreation. When an instantaneous contaminant is released into a wetland, both boundary absorptions and wind have a significant impact on the contaminant dispersion process. The present study highlights the wind effects and boundary absorption on environmental dispersion and stream-wise mean concentration in a width-dominated flow for a shallow wetland. A multi-scale time period is considered to determine the dispersion model, which illustrates the contaminant transport process greatly affected by different ecological parameters like dispersion time, boundary absorption, tortuosity and vegetation drag. It is noted that the stream-wise concentration distribution is almost identical to the transversal concentration distribution due to tortuosity and bottom vegetation effect. It is noticed that the stream-wise concentration of contaminant gradually decreases due to the increase of vegetation parameters and tortuosity. The distribution of flow velocity is derived analytically from the momentum equation for different wind strengths. The effect of boundary absorption strength on stream-wise and mean concentration are discussed. Also, it is evident that the distribution of concentration is complex for boundary absorption under the effects of wind.

Spatiotemporal variations of biomass burning (BB) over the Indian region using satellite-based data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003–2021 are analyzed and studied. We have used fire products with a high confidence level (≥ 80%), which is free from false alarm fires. The total fire counts (TFC), fire radiative power (FRP), and burned area (BA) for different land use and land cover (LULC) types over six different regions, namely Central India (CI), Indian Gangetic Plain (IGP), North-East India (NEI), North India (NI), South India (SI) and West India (WI) are studied. The biomass burning shows spatial, seasonal and inter-annual variations. Within the regions, different hotspots are identified for cropland burning, forest burning, etc. It is observed that in the IGP and WI regions, burning activity shows bi-modal seasonal behaviour, which coincides with crop burning after harvesting seasons, while other regions show a single mode. Non-parametric long-term analysis in TFC and TFRP (derived by adding FRP of all the fire hotspots in a respective year) shows a positive trend over all the regions except in the NEI region. The decreasing TFC with increasing precipitation is also observed in all the considered regions, which is attributed to enhanced moisture and decreased temperature. The present study provides the scientific basis for addressing the origin and type of biomass burning in different regions of India, and it is quite useful for developing procedures, awareness, and planning for reducing BB, which is quite harmful to human health as well as the environment.